Air quality Prediction using Deep Learning Techniques
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Date
2024-06
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Avinashilingam
Abstract
Machine Learning models and Deep Learning models have been widely used to predict
the air quality. Monitoring air quality involves both regulatory measures and public awareness
campaigns to reduce emissions from various sources such as vehicles, industrial activities,
agriculture, and household combustion. Air pollution predict is very useful for informing about
the pollution level that allow policy makers to adopt measures for reducing its impact.
Over the past few decades, due to human activities, industrialization, and urbanization,
air quality condition has become a life-threatening factor in many countries around the world.
It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is
necessary to accurately predict the PM2.5 concentrations in order to prevent the citizens from
the dangerous impact of air pollution. Air pollution refers to the presence of harmful or
excessive quantities of substances in the air we breathe, which can be detrimental to human
health, the environment and ecosystems. These substances, known as pollutants, can come
from various sources, including industrial activities, vehicle emissions, agricultural practices,
and natural phenomena. Common air pollutants include particulate matter (PM), nitrogen
oxides (NOx), sulfur dioxide (SO2), carbon monoxide (CO), volatile organic compounds
(VOCs), and ozone (O3). The air quality prediction is used to predict the future state of air
quality in a particular location based on the existing data, such as historical air quality data.
In the first phase of the research work, an Improved Sparse Auto Encoder-Deep
Learning Algorithm (ISAE-DL) is used to predict the air quality system and the feed forward
neural network is utilized as a sparse auto encoder. The combined k-Nearest Neighbor
Euclidean Distance (kNN-ED) and kNN- Dynamic Time Warping Distance (kNN-DTWD) is
used to acquire the particulate matter and the meteorological data. In addition, Artificial
Neural Network (ANN) and Long Short-Term Memory (LSTM) are used to acquire the
relative information and the classification model is generated with training data.
In the second phase of research work, Voronoi Clustering Sparse Auto Encoder- Deep
Learning (VCSAE-DL) is developed to handle the long time delay based locations for better
air quality prediction. Then, the temporal and spatial features are identified to retrieve the most
important features for air quality prediction. The formation of clusters is continued with
different centers and the clustering process is stopped until all the data are covered. The
clustered data and the terrain information are given as input to the Neural Network layer such
as Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Long Short-
Term Memory (LSTM) and their results are combined and transferred to Sparse Auto encoder
for the prediction of air quality. This method efficiently reduces the long-term delay issues, but
this method can also suffer to learn from the long-term dependencies of air pollutant
concentrations.
In the third phase of research work, a Transferred Stacked Bidirectional and
Unidirectional Long Short-Term memory (T-SBU-LSTM) is proposed to minimize the long
term dependencies for LSTM for air quality prediction. Then, the Transferred Stacked
Bidirectional and Unidirectional LSTM (T-SBU-LSTM) was adopted in learning from long-
term PM2.5 dependencies, and it uses Transfer learning to transfer knowledge from smaller
temporal resolutions to higher temporal resolutions. Transfer learning is used to improve
prediction accuracy at higher temporal resolutions which identifies the similarities between
two separate datasets, tasks, or models to transmit data from the source to the new domain.
This combined architecture enhances the feature learning from the large-scale spatial-
temporal time series data by learning both forwards and backward dependencies. This phase
of research expands the air quality prediction from a specific location to several adjacent
locations varying small period to long period time delays.
In the fourth phase of work, Wasserstein Distance - Deep Transfer Learning (WD-
DTL) is proposed to reduce the learning time of Transfer Learning. Then, the Wasserstein
distance based Deep Transfer Learning (WD-DTL) is constructed to learn invariant features
between source and target domains. Initially, a base LSTM model is trained with sufficient
data in source domain.
Finally, the developed approaches like Improved Sparse Auto Encoder Using Deep
Learning (ISAE-DL), Voronoi Clustering Sparse Auto Encoder Using Deep Learning
(VCSAE-DL), Transferred Stacked Bidirectional and Unidirectional Using Long Short Term
Memory Algorithm (T-SBU-LSTM) and Wasserstein distance using Deep Transfer Learning
(WD-DTL) based air quality prediction system were compared using the performance metrics,
Accuracy, Precision, Specificity, Sensitivity, AUC, MCC and MAER. The experimental results
proved that WD-DTL based air quality prediction system accomplishes better than the other
prediction algorithms.
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Computer Science and Engineering